Beyond 5G Networks: Integration of Communication, Computing, Caching, and Control

In recent years,the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks.Such challenges can be potentially overcome by integrating communication,computing,caching,and control(i4C)technologies.In this survey,we first give a snapshot of different aspects of the i4C,comprising background,motivation,leading technological enablers,potential applications,and use cases.Next,we describe different models of communication,computing,caching,and control(4C)to lay the foundation of the integration approach.We review current stateof-the-art research efforts related to the i4C,focusing on recent trends of both conventional and artificial intelligence(AI)-based integration approaches.We also highlight the need for intelligence in resources integration.Then,we discuss the integration of sensing and communication(ISAC)and classify the integration approaches into various classes.Finally,we propose open challenges and present future research directions for beyond 5G networks,such as 6G.

Application of improved virtual sample and sparse representation in face recognition

Sparse representation plays an important role in the research of face recognition.As a deformable sample classification task,face recognition is often used to test the performance of classification algorithms.In face recognition,differences in expression,angle,posture,and lighting conditions have become key factors that affect recognition accuracy.Essentially,there may be significant differences between different image samples of the same face,which makes image classification very difficult.Therefore,how to build a robust virtual image representation becomes a vital issue.To solve the above problems,this paper proposes a novel image classification algorithm.First,to better retain the global features and contour information of the original sample,the algorithm uses an improved non‐linear image representation method to highlight the low‐intensity and high‐intensity pixels of the original training sample,thus generating a virtual sample.Second,by the principle of sparse representation,the linear expression coefficients of the original sample and the virtual sample can be calculated,respectively.After obtaining these two types of coefficients,calculate the distances between the original sample and the test sample and the distance between the virtual sample and the test sample.These two distances are converted into distance scores.Finally,a simple and effective weight fusion scheme is adopted to fuse the classification scores of the original image and the virtual image.The fused score will determine the final classification result.The experimental results show that the proposed method outperforms other typical sparse representation classification methods.

Computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture

Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)approaches.Weed plays a vital role in influencing crop productivity.The wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are increased.Since the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity,this study presents a novel computer vision and deep learning based weed detection and classification(CVDL-WDC)model for precision agriculture.The proposed CVDL-WDC technique intends to prop-erly discriminate the plants as well as weeds.The proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine(ELM)based weed classification.The parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization(FFO)algorithm.A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced out-comes over its recent approaches interms of several measures.

Estrada index of dynamic random graphs

The Estrada index of a graph G on n vertices is defined by EE(G)=∑^(n)_(i=1)^(eλ_(i)),whereλ_(1),λ_(2),···,λ_(n)are the adjacency eigenvalues of G.We define two general types of dynamic graphs evolving according to continuous-time Markov processes with their stationary distributions matching the Erd¨os-R´enyi random graph and the random graph with given expected degrees,respectively.We formulate some new estimates and upper and lower bounds for the Estrada indices of these dynamic graphs.

A deep convolutional neural network for diabetic retinopathy detection via mining local and long-range dependence

Diabetic retinopathy(DR),the main cause of irreversible blindness,is one of the most common complications of diabetes.At present,deep convolutional neural networks have achieved promising performance in automatic DR detection tasks.The convolution operation of methods is a local cross-correlation operation,whose receptive field de-termines the size of the local neighbourhood for processing.However,for retinal fundus photographs,there is not only the local information but also long-distance dependence between the lesion features(e.g.hemorrhages and exudates)scattered throughout the whole image.The proposed method incorporates correlations between long-range patches into the deep learning framework to improve DR detection.Patch-wise re-lationships are used to enhance the local patch features since lesions of DR usually appear as plaques.The Long-Range unit in the proposed network with a residual structure can be flexibly embedded into other trained networks.Extensive experimental results demon-strate that the proposed approach can achieve higher accuracy than existing state-of-the-art models on Messidor and EyePACS datasets.

Combined Effect of Concept Drift and Class Imbalance on Model Performance During Stream Classification

Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes overtime, leading to class imbalance and concept drift issues. Both these issuescause model performance degradation. Most of the current work has beenfocused on developing an ensemble strategy by training a new classifier on thelatest data to resolve the issue. These techniques suffer while training the newclassifier if the data is imbalanced. Also, the class imbalance ratio may changegreatly from one input stream to another, making the problem more complex.The existing solutions proposed for addressing the combined issue of classimbalance and concept drift are lacking in understating of correlation of oneproblem with the other. This work studies the association between conceptdrift and class imbalance ratio and then demonstrates how changes in classimbalance ratio along with concept drift affect the classifier’s performance.We analyzed the effect of both the issues on minority and majority classesindividually. To do this, we conducted experiments on benchmark datasetsusing state-of-the-art classifiers especially designed for data stream classification.Precision, recall, F1 score, and geometric mean were used to measure theperformance. Our findings show that when both class imbalance and conceptdrift problems occur together the performance can decrease up to 15%. Ourresults also show that the increase in the imbalance ratio can cause a 10% to15% decrease in the precision scores of both minority and majority classes.The study findings may help in designing intelligent and adaptive solutionsthat can cope with the challenges of non-stationary data streams like conceptdrift and class imbalance.

Comparison of Websites Employing Search Engine Optimization and Live Data

This study compares websites that take live data into account using search engine optimization(SEO).A series of steps called search engine optimization can help a website rank highly in search engine results.Static websites and dynamic websites are two different types of websites.Static websites must have the necessary expertise in programming compatible with SEO.Whereas in dynamic websites,one can utilize readily available plugins/modules.The fundamental issue of all website holders is the lower level of page rank,congestion,utilization,and exposure of the website on the search engine.Here,the authors have studied the live data of four websites as the real-time data would indicate how the SEO strategy may be applied to website page rank,page difficulty removal,and brand query,etc.It is also necessary to choose relevant keywords on any website.The right keyword might assist to increase the brand query while also lowering the page difficulty both on and off the page.In order to calculate Off-page SEO,On-page SEO,and SEO Difficulty,the authors examined live data in this study and chose four well-known Indian university and institute websites for this study:www.caluniv.ac.in,www.jnu.ac.in,www.iima.ac.in,and www.iitb.ac.in.Using live data and SEO,the authors estimated the Off-page SEO,On-page SEO,and SEO Difficulty.It has been shown that the Off-page SEO of www.caluniv.ac.in is lower than that of www.jnu.ac.in,www.iima.ac.in,and www.iitb.ac.in by 9%,7%,and 7%,respectively.On-page SEO is,in comparison,4%,1%,and 1%more.Every university has continued to keep up its own brand query.Additionally,www.caluniv.ac.in has slightly less SEO Difficulty compared to other websites.The final computed results have been displayed and compared.

Analysis on unit maximum capacity of orthogonal multiple watermarking for multimedia signals in B5G wireless communications

Beyond-5G(B5G)aims to meet the growing demands of mobile traffic and expand the communication space.Considering that intelligent applications to B5G wireless communications will involve security issues regarding user data and operational data,this paper analyzes the maximum capacity of the multi-watermarking method for multimedia signal hiding as a means of alleviating the information security problem of B5G.The multiwatermarking process employs spread transform dither modulation.During the watermarking procedure,Gram-Schmidt orthogonalization is used to obtain the multiple spreading vectors.Consequently,multiple watermarks can be simultaneously embedded into the same position of a multimedia signal.Moreover,the multiple watermarks can be extracted without affecting one another during the extraction process.We analyze the effect of the size of the spreading vector on the unit maximum capacity,and consequently derive the theoretical relationship between the size of the spreading vector and the unit maximum capacity.A number of experiments are conducted to determine the optimal parameter values for maximum robustness on the premise of high capacity and good imperceptibility.

Integrated Clustering and Routing Design and Triangle Path Optimization for UAV-Assisted Wireless Sensor Networks

With the development of the Internet of Things(IoT),it requires better performance from wireless sensor networks(WSNs),such as larger coverage,longer lifetime,and lower latency.However,a large amount of data generated from monitoring and long-distance transmission places a heavy burden on sensor nodes with the limited battery power.For this,we investigate an unmanned aerial vehicles assisted mobile wireless sensor network(UAV-assisted WSN)to prolong the network lifetime in this paper.Specifically,we use UAVs to assist the WSN in collecting data.In the current UAV-assisted WSN,the clustering and routing schemes are determined sequentially.However,such a separate consideration might not maximize the lifetime of the whole WSN due to the mutual coupling of clustering and routing.To efficiently prolong the lifetime of the WSN,we propose an integrated clustering and routing scheme that jointly optimizes the clustering and routing together.In the whole network space,it is intractable to efficiently obtain the optimal integrated clustering and routing scheme.Therefore,we propose the Monte-Las search strategy based on Monte Carlo and Las Vegas ideas,which can generate the chain matrix to guide the algorithm to find the solution faster.Unnecessary point-to-point collection leads to long collection paths,so a triangle optimization strategy is then proposed that finds a compromise path to shorten the collection path based on the geometric distribution and energy of sensor nodes.To avoid the coverage hole caused by the death of sensor nodes,the deployment of mobile sensor nodes and the preventive mechanism design are indispensable.An emergency data transmission mechanism is further proposed to reduce the latency of collecting the latency-sensitive data due to the absence of UAVs.Compared with the existing schemes,the proposed scheme can prolong the lifetime of the UAVassisted WSN at least by 360%,and shorten the collection path of UAVs by 56.24%.

A Systematic Literature Review on Blockchain Consensus Mechanisms’ Security: Applications and Open Challenges

This study conducts a systematic literature review(SLR)of blockchain consensus mechanisms,an essential protocols that maintain the integrity,reliability,and decentralization of distributed ledger networks.The aim is to comprehensively investigate prominent mechanisms’security features and vulnerabilities,emphasizing their security considerations,applications,challenges,and future directions.The existing literature offers valuable insights into various consensus mechanisms’strengths,limitations,and security vulnerabilities and their real-world applications.However,there remains a gap in synthesizing and analyzing this knowledge systematically.Addressing this gap would facilitate a structured approach to understanding consensus mechanisms’security and vulnerabilities comprehensively.The study adheres to Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines and computer science standards and reviewed 3749 research papers from 2016 to 2024,excluding grey literature,resulting in 290 articles for descriptive analysis.The research highlights an increased focus on blockchain consensus security,energy efficiency,and hybrid mechanisms within 60%of research papers post-2019,identifying gaps in scalability,privacy,and interoperability for future exploration.By synthesizing the existing research and identifying the key trends,this SLR contributes to advancing the understanding of blockchain consensus mechanisms’security and guiding future research and structured innovation in blockchain systems and applications.

A Survey of Human-centered Intelligent Robots:Issues and Challenges

Intelligent techniques foster the dissemination of new discoveries and novel technologies that advance the ability of robots to assist and support humans. The human-centered intelligent robot has become an important research field that spans all of the robot capabilities including navigation, intelligent control, pattern recognition and human-robot interaction. This paper focuses on the recent achievements and presents a survey of existing works on human-centered robots. Furthermore, we provide a comprehensive survey of the recent development of the human-centered intelligent robot and discuss the issues and challenges in the field.

Path Planning and Navigation of Oceanic Autonomous Sailboats and Vessels: A Survey

Oceanic autonomous surface vehicles(ASVs) are one kind of autonomous marine robots that have advantages of energy saving and is flexible to use. Nowadays, ASVs are playing an important role in marine science, maritime industry, and national defense. It could improve the efficiency of oceanic data collection, ensure marine transportation safety, and protect national security. One of the core challenges for ASVs is how to plan a safe navigation autonomously under the complicated ocean environment. Based on the type of marine vehicles, ASVs could be divided into two categories: autonomous sailboats and autonomous vessels. In this article, we review the challenges and related solutions of ASVs' autonomous navigation, including modeling analysis, path planning and implementation. Finally, we make a summary of all of those in four tables and discuss about the future research directions.

Target tracking method of Siamese networks based on the broad learning system

Target tracking has a wide range of applications in intelligent transportation,real‐time monitoring,human‐computer interaction and other aspects.However,in the tracking process,the target is prone to deformation,occlusion,loss,scale variation,background clutter,illumination variation,etc.,which bring great challenges to realize accurate and real‐time tracking.Tracking based on Siamese networks promotes the application of deep learning in the field of target tracking,ensuring both accuracy and real‐time performance.However,due to its offline training,it is difficult to deal with the fast motion,serious occlusion,loss and deformation of the target during tracking.Therefore,it is very helpful to improve the performance of the Siamese networks by learning new features of the target quickly and updating the target position in time online.The broad learning system(BLS)has a simple network structure,high learning efficiency,and strong feature learning ability.Aiming at the problems of Siamese networks and the characteristics of BLS,a target tracking method based on BLS is proposed.The method combines offline training with fast online learning of new features,which not only adopts the powerful feature representation ability of deep learning,but also skillfully uses the BLS for re‐learning and re‐detection.The broad re‐learning information is used for re‐detection when the target tracking appears serious occlusion and so on,so as to change the selection of the Siamese networks search area,solve the problem that the search range cannot meet the fast motion of the target,and improve the adaptability.Experimental results show that the proposed method achieves good results on three challenging datasets and improves the performance of the basic algorithm in difficult scenarios.

Usability and Effectiveness of Mobile Learning Course Content Application as a Revision Tool

The use of mobile phone technologies in the education sector is getting more attention nowadays. This is due to the advancement of technologies equipped in majority of the mobile phones which makes the devices become more capable of supporting the learning and teaching activities. Mobile learning （m-learning） is a learning tool which can be run on mobile devices. It can be considered as an enhancement to the electronic learning （e-learning）. M-learning overcomes several limitations of e-learning especially in term of mobility. It provides more independent way of learning whereby learners can use the application to do the learning activities at anytime and any place. However, as with other learning and teaching applications, applications to be developed for mobile learning must also be developed based on certain learning theories and guidelines in order for them to be effective as well as usable. Therefore, in this paper, the development process of a mobile learning course content application called Mobile System Analysis and Design （MOSAD） as a revision tool will be shared and its testing＇s conduct and results will also be presented and discussed. MOSAD was developed with the content of a topic from the System Analysis and Design （SAD） course conducted at Universiti Teknologi PETRONAS （UTP）. A heuristic test involving 5 experts in the area of Human Computer Interaction （HCI） were conducted after the first version of MOSAD was completed to strengthen its functionality and usability, followed by a Post Test Quasi Experimental Design which was conducted to 116 UTP second year students who took the SAD course to test the effectiveness and usability of MOSAD after it was revised. As a result from the post test, the students who had used MOSAD （66 out of the 116 students） as their revision tool for answering ten quiz questions obtained a mean score of 7.7576 as compared to 5.160 obtained by the other group of students （50 out of the 116 students） who used traditional methods of revision. Besides, usability test which tested on consistency, leamability, flexibility, minimal action and minimal memory load of MOSAD gave results above 3.5 for each metric based on the rating of 1 to 5. Thus, both results indicate that MOSAD is effective and usable as a revision tool for the higher education students.

Automated Colorization of Grayscale Images Using Texture Descriptors and a Modified Fuzzy C-Means Clustering

A novel example-based process for Automated Colorization of grayscale images using Texture Descriptors (ACTD) without any human intervention is proposed. By analyzing a set of sample color images, coherent regions of homogeneous textures are extracted. A multi-channel filtering technique is used for texture-based image segmentation, combined with a modified Fuzzy C-means (FCM) clustering algorithm. This modified FCM clustering algorithm includes both the local spatial information from neighboring pixels, and the spatial Euclidian distance to the cluster’s center of gravity. For each area of interest, state-of-the-art texture descriptors are then computed and stored, along with corresponding color information. These texture descriptors and the color information are used for colorization of a grayscale image with similar textures. Given a grayscale image to be colorized, the segmentation and feature extraction processes are repeated. The texture descriptors are used to perform Content-Based Image Retrieval (CBIR). The colorization process is performed by Chroma replacement. This research finds numerous applications, ranging from classic film restoration and enhancement, to adding valuable information into medical and satellite imaging. Also, this can be used to enhance the detection of objects from x-ray images at the airports.

Real-Time Detection of Human Drowsiness via a Portable Brain-Computer Interface

In this paper, we proposed a new concept: depth of drowsiness, which can more precisely describe the drowsiness than existing binary description. A set of effective markers for drowsiness: normalized band norm was successfully developed. These markers are invariant from voltage amplitude of brain waves, eliminating the need for calibrating the voltage output of the brain-computer interface devices. A new polling algorithm was designed and implemented for computing the depth of drowsiness. The time cost of data acquisition and processing for each estimate is about one second, which is well suited for real-time applications. Test results with a portable brain-computer interface device show that the depth of drowsiness computed by the method in this paper is generally invariant from ages of test subjects and sensor channels (P3 and C4). The comparison between experiment and computing results indicate that the new method is noticeably better than one of the recent methods in terms of accuracy for predicting the drowsiness.

Optimal Deep Belief Network Enabled Malware Detection and Classification Model

Cybercrime has increased considerably in recent times by creating new methods of stealing,changing,and destroying data in daily lives.Portable Docu-ment Format(PDF)has been traditionally utilized as a popular way of spreading malware.The recent advances of machine learning(ML)and deep learning(DL)models are utilized to detect and classify malware.With this motivation,this study focuses on the design of mayfly optimization with a deep belief network for PDF malware detection and classification(MFODBN-MDC)technique.The major intention of the MFODBN-MDC technique is for identifying and classify-ing the presence of malware exist in the PDFs.The proposed MFODBN-MDC method derives a new MFO algorithm for the optimal selection of feature subsets.In addition,Adamax optimizer with the DBN model is used for PDF malware detection and classification.The design of the MFO algorithm to select features and Adamax based hyperparameter tuning for PDF malware detection and classi-fication demonstrates the novelty of the work.For demonstrating the improved outcomes of the MFODBN-MDC model,a wide range of simulations are exe-cuted,and the results are assessed in various aspects.The comparison study high-lighted the enhanced outcomes of the MFODBN-MDC model over the existing techniques with maximum precision,recall,and F1 score of 97.42%,97.33%,and 97.33%,respectively.

Classification of Adversarial Attacks Using Ensemble Clustering Approach

As more business transactions and information services have been implemented via communication networks,both personal and organization assets encounter a higher risk of attacks.To safeguard these,a perimeter defence likeNIDS(network-based intrusion detection system)can be effective for known intrusions.There has been a great deal of attention within the joint community of security and data science to improve machine-learning based NIDS such that it becomes more accurate for adversarial attacks,where obfuscation techniques are applied to disguise patterns of intrusive traffics.The current research focuses on non-payload connections at the TCP(transmission control protocol)stack level that is applicable to different network applications.In contrary to the wrapper method introduced with the benchmark dataset,three new filter models are proposed to transform the feature space without knowledge of class labels.These ECT(ensemble clustering based transformation)techniques,i.e.,ECT-Subspace,ECT-Noise and ECT-Combined,are developed using the concept of ensemble clustering and three different ensemble generation strategies,i.e.,random feature subspace,feature noise injection and their combinations.Based on the empirical study with published dataset and four classification algorithms,new models usually outperform that original wrapper and other filter alternatives found in the literature.This is similarly summarized from the first experiment with basic classification of legitimate and direct attacks,and the second that focuses on recognizing obfuscated intrusions.In addition,analysis of algorithmic parameters,i.e.,ensemble size and level of noise,is provided as a guideline for a practical use.

Multimodal Machine Learning Based Crop Recommendation and Yield Prediction Model

Agriculture plays a vital role in the Indian economy.Crop recommen-dation for a specific region is a tedious process as it can be affected by various variables such as soil type and climatic parameters.At the same time,crop yield prediction was based on several features like area,irrigation type,temperature,etc.The recent advancements of artificial intelligence(AI)and machine learning(ML)models pave the way to design effective crop recommendation and crop pre-diction models.In this view,this paper presents a novel Multimodal Machine Learning Based Crop Recommendation and Yield Prediction(MMML-CRYP)technique.The proposed MMML-CRYP model mainly focuses on two processes namely crop recommendation and crop prediction.At the initial stage,equilibrium optimizer(EO)with kernel extreme learning machine(KELM)technique is employed for effectual recommendation of crops.Next,random forest(RF)tech-nique was executed for predicting the crop yield accurately.For reporting the improved performance of the MMML-CRYP system,a wide range of simulations were carried out and the results are investigated using benchmark dataset.Experi-mentation outcomes highlighted the significant performance of the MMML-CRYP approach on the compared approaches with maximum accuracy of 97.91%.